ABSTRACT
Context In low-income settings where access to biological diagnosis is limited, data on the spread of the COVID-19 epidemic are scarce. In September 2020, after the first COVID-19 wave, Mali reported 3,086 confirmed cases and 130 deaths. Most reports originated form Bamako, the capital city, with 1,532 reported cases and 81 deaths for an estimated 2.42 million population. This observed prevalence of 0.06% appeared very low. Our objective was to estimate SARS-CoV-2 infection among inhabitants of Bamako, after the first epidemic wave. We also assessed demographic, social and living conditions, health behaviors and knowledge associated with SARS-CoV-2 seropositivity. Material and methods We conducted a cross-sectional multistage cluster household survey in commune VI, which reported, at this time, 30% (n=466) of the total cases reported at Bamako. We measured serological status by detection of SARS-CoV-2 spike protein Antibodies in venous blood sampled after informed consent. We documented housing conditions and individual health behaviors through KABP questionnaires among participants aged 12 years and older. We estimated the number of SARS-CoV-2 infections and deaths in the total population of Bamako using the age and sex distributions of SARS-CoV-2 seroprevalence. A logistic generalized additive multilevel model was performed to estimate household conditions and demographic factors associated with seropositivity. Results We recruited 1,526 inhabitants in the 3 investigated areas (commune VI, Bamako) belonging to the 306 sampled households. We obtained 1,327 serological results, 220 household questionnaires and collected KABP answers for 962 participants. The prevalence of SARS-CoV-2 seropositivity was 16.4% after adjusting on the population structure. This suggested that ~400,000 cases and ~ 2,000 deaths could have occurred of which only 0.4% of cases and 5% of deaths were officially reported. KABP analyses suggested strong agreement with washing hands but lower acceptability of movement restrictions (lockdown or curfew), and limited mask wearing. Conclusion In spite of limited numbers of reported cases, the first wave of SARS-CoV-2 spread broadly in Bamako. Expected fatalities remained limited largely due to the population age structure and the low prevalence of comorbidities. This highlight the difficulty of developing epidemic control strategies when screening test are not available or not used, even more when the transmission modalities are not well known by the population. Targeted policies based on health education prevention have to be implemented to improve the COVID-19 risk perception among the local population and fight to false knowledge and beliefs.
Subject(s)
COVID-19 , Severe Acute Respiratory SyndromeABSTRACT
The novel coronavirus (2019-nCoV) epidemic has spread to 23 countries from China. Local cycles of transmission already occurred in 7 countries following case importation. No African country has reported cases yet. The management and control of 2019-nCoV introductions heavily relies on the public health capacity of a country. Here we evaluate the preparedness and vulnerability of African countries against their risk of importation of 2019-nCoV. We used data on air travel volumes departing from airports in the infected provinces in China and directed to Africa to estimate the risk of introduction per country. We determined the countries capacity to detect and respond to cases with two indicators: preparedness, using the WHO International Health Regulation Monitoring and Evaluation Framework; and vulnerability, with the Infectious Disease Vulnerability Index. Countries were clustered according to the Chinese regions contributing the most to their risk. Findings: Countries at the highest importation risk (Egypt, Algeria, Republic of South Africa) have moderate to high capacity to respond to outbreaks. Countries at moderate risk (Nigeria, Ethiopia, Sudan, Angola, Tanzania, Ghana, Kenya) have variable capacity and high vulnerability. Three clusters of countries are identified that share the same exposure to the risk originating from the provinces of Guangdong, Fujian, and Beijing, respectively. Interpretation: Several countries in Africa are stepping up their preparedness to detect and cope with 2019-nCoV importations. Resources and intensified surveillance and capacity capacity should be urgently prioritized towards countries at moderate risk that may be ill-prepared to face the importation and to limit onward transmission.